Skip to main content
Springer Nature Link
Log in

Can Concept-Based User Modeling Improve Adaptive Visualization?

  • Conference paper
User Modeling, Adaptation, and Personalization (UMAP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6075))

Abstract

Adaptive visualization can present user-adaptive information in such a way as to help users to analyze complicated information spaces easily and intuitively. We presented an approach called Adaptive VIBE, which extended the traditional reference point-based visualization algorithm, so that it could adaptively visualize documents of interest. The adaptive visualization was implemented by separating the effects of user models and queries within the document space and we were able to show the potential of the proposed idea. However, adaptive visualization still remained in the simple bag-of-words realm. The keywords used to construct the user models were not effective enough to express the concepts that need to be included in the user models. In this study, we tried to improve the old-fashioned keyword-only user models by adopting more concept-rich named-entities. The evaluation results show the strengths and shortcomings of using named-entities as conceptual elements for visual user models and the potential to improve the effectiveness of personalized information access systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ahn, J., Brusilovsky, P.: Adaptive visualization of search results: Bringing user models to visual analytics. Information Visualization 8(3), 167–179 (2009)

    Article  Google Scholar 

  2. Ahn, J., Brusilovsky, P., Grady, J., He, D., Florian, R.: Semantic annotation based exploratory search for information analysts. In: Information Processing and Management (in press, 2010)

    Google Scholar 

  3. Ahn, J., Brusilovsky, P., He, D., Grady, J., Li, Q.: Personalized web exploration with task models. In: Huai, J., Chen, R., Hon, H.W., Liu, Y., Ma, W.Y., Tomkins, A., Zhang, X. (eds.) Proceedings of the 17th International Conference on World Wide Web, WWW 2008, Beijing, China, April 21-25, pp. 1–10. ACM, New York (2008)

    Chapter  Google Scholar 

  4. Bier, E.A., Ishak, E.W., Chi, E.: Entity workspace: An evidence file that aids memory, inference, and reading. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B.M., Wang, F.Y. (eds.) ISI 2006. LNCS, vol. 3975, pp. 466–472. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Chen, C.C., Chen, M.C., Sun, Y.: Pva: a self-adaptive personal view agent system. In: KDD ’01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 257–262. ACM Press, New York (2001)

    Chapter  Google Scholar 

  6. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 1(2), 224–227 (2009)

    Article  Google Scholar 

  7. Florian, R., Hassan, H., Ittycheriah, A., Jing, H., Kambhatla, N., Luo, X., Nicolov, H., Roukos, S., Zhang, T.: A statistical model for multilingual entity detection and tracking. In: Proceedings of the Human Language Technologies Conference (HLT-NAACL’04), Boston, MA, USA, May 2004, pp. 1–8 (2004)

    Google Scholar 

  8. Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Gentili, G., Micarelli, A., Sciarrone, F.: Infoweb: An adaptive information filtering system for the cultural heritage domain. Applied Artificial Intelligence 17(8-9), 715–744 (2003)

    Article  Google Scholar 

  10. Hanani, U., Shapira, B., Shoval, P.: Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction 11(3), 203–259 (2001)

    Article  MATH  Google Scholar 

  11. Korfhage, R.R.: To see, or not to see – is that the query? In: SIGIR ’91: Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 134–141. ACM, New York (1991)

    Chapter  Google Scholar 

  12. Kumaran, G., Allan, J.: Text classification and named entities for new event detection. In: SIGIR ’04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 297–304. ACM, New York (2004)

    Google Scholar 

  13. Leuski, A., Allan, J.: Interactive information retrieval using clustering and spatial proximity. User Modeling and User-Adapted Interaction 14(2-3), 259–288 (2004)

    Article  Google Scholar 

  14. Magnini, B., Strapparava, C.: User modelling for news web sites with word sense based techniques. User Modeling and User-Adapted Interaction 14(2), 239–257 (2004)

    Article  Google Scholar 

  15. Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)

    Article  Google Scholar 

  16. Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S.: Personalized search on the world wide web. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 195–230. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  17. Mihalcea, R., Moldovan, D.L.: Document indexing using named entities. Studies in Informatics and Control 10(1), 21–28 (2001)

    Google Scholar 

  18. Olsen, K.A., Korfhage, R., Sochats, K.M., Spring, M.B., Williams, J.G.: Visualization of a document collection: The vibe system. Information Processing and Management 29(1), 69–81 (1993)

    Article  Google Scholar 

  19. Petkova, D., Croft, B.W.: Proximity-based document representation for named entity retrieval. In: CIKM ’07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp. 731–740. ACM, New York (2007)

    Chapter  Google Scholar 

  20. Pretschner, A., Gauch, S.: Ontology based personalized search. In: 11th IEEE Intl. Conf. on Tools with Artificial Intelligence (ICTAI’99), Chicago, IL, pp. 391–398 (1999)

    Google Scholar 

  21. Roussinov, D., Ramsey, M.: Information forage through adaptive visualization. In: DL ’98: Proceedings of the third ACM conference on Digital libraries, pp. 303–304. ACM, New York (1998)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, 15260

    Jae-wook Ahn & Peter Brusilovsky

Authors
  1. Jae-wook Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Brusilovsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science Department, Eindhoven University of Technology, 5600, Eindhoven, MB, The Netherlands

    Paul De Bra

  2. School of Information and Computer Sciences, University of California, 92697-3440, Irvine, CA, USA

    Alfred Kobsa

  3. Department of Information and Computer Sciences, University of Hawaii, 96822, Honolulu, HI, USA

    David Chin

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ahn, Jw., Brusilovsky, P. (2010). Can Concept-Based User Modeling Improve Adaptive Visualization?. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13470-8_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13469-2

  • Online ISBN: 978-3-642-13470-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics